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At least 325 records · Page 18Linked to original sources

USGS2023

High-frequency time series comparison of Sentinel-1 and Sentinel-2 for open and vegetated water across the United States (2017-2021)

Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km 2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017–2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

continental United States↗

USGS1992

The 1990 conterminous U. S. AVHRR data set

The U.S. Geological Survey, using NOAA-ll Advanced Very High Resolution Radiometer (AVHRR) 1-km data, has produced a time series of 19 biweekly maximum normalized difference vegetation index (NDV!) composites of the conterminous United States for the 1990 growing season. Each biweekly composite included data from approximately 20 calibrated and georegistered daily overpasses. The output is a data set which includes all five calibrated AVHRR channels, NOV! values, three satellite/solar viewing angles, and date of observation pointer for each biweekly composite. The data set is intended for assessing seasonal variations in vegetation condition and provides a foundation for studying long-term changes in vegetation resulting from human interactions or global climate alterations.

Photogrammetric Engineering and Remote Sensing↗

USGS2010

Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States

Since January 2008, the U.S. Department of Interior / U.S. Geological Survey have been providing free terrain-corrected (Level 1T) Landsat Enhanced Thematic Mapper Plus (ETM+) data via the Internet, currently for acquisitions with less than 40% cloud cover. With this rich dataset, temporally composited, mosaics of the conterminous United States (CONUS) were generated on a monthly, seasonal, and annual basis using 6521 ETM+ acquisitions from December 2007 to November 2008. The composited mosaics are designed to provide consistent Landsat data that can be used to derive land cover and geo-physical and bio-physical products for detailed regional assessments of land-cover dynamics and to study Earth system functioning. The data layers in the composited mosaics are defined at 30 m and include top of atmosphere (TOA) reflectance, TOA brightness temperature, TOA normalized difference vegetation index (NDVI), the date each composited pixel was acquired on, per-band radiometric saturation status, cloud mask values, and the number of acquisitions considered in the compositing period. Reduced spatial resolution browse imagery, and top of atmosphere 30 m reflectance time series extracted from the monthly composites, capture the expected land surface phenological change, and illustrate the potential of the composited mosaic data for terrestrial monitoring at high spatial resolution. The composited mosaics are available in 501 tiles of 5000 × 5000 30 m pixels in the Albers equal area projection and are downloadable at http://landsat.usgs.gov/WELD.php . The research described in this paper demonstrates the potential of Landsat data processing to provide a consistent, long-term, large-area, data record.

Remote Sensing of Environment↗

USGS2017

The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States

The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (https://doi. org/10.5066/F7WH2N65) represents a seamless, spatial database of 48 State geologic maps that range from 1:50,000 to 1:1,000,000 scale. A national digital geologic map database is essential in interpreting other datasets that support numerous types of national-scale studies and assessments, such as those that provide geochemistry, remote sensing, or geophysical data. The SGMC is a compilation of the individual U.S. Geological Survey releases of the Preliminary Integrated Geologic Map Databases for the United States. The SGMC geodatabase also contains updated data for seven States and seven entirely new State geologic maps that have been added since the preliminary databases were published. Numerous errors have been corrected and enhancements added to the preliminary datasets using thorough quality assurance/quality control procedures. The SGMC is not a truly integrated geologic map database because geologic units have not been reconciled across State boundaries. However, the geologic data contained in each State geologic map have been standardized to allow spatial analyses of lithology, age, and stratigraphy at a national scale.

Data Series↗

USGS2022

Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption

Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.

Alaska↗

USGS2021

Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX)

The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.

Frontiers in Climate↗

USGS2004

A geometric performance assessment of the EO-1 advanced land imager

The Earth Observing 1 (EO-1) Advanced Land Imager (ALI) demonstrates technology applicable to a successor system to the Landsat Thematic Mapper series. A study of the geometric performance characteristics of the ALI was conducted under the auspices of the EO-1 Science Validation Team. This study evaluated ALI performance with respect to absolute pointing knowledge, focal plane sensor chip assembly alignment, and band-to-band registration for purposes of comparing this new technology to the heritage Landsat systems. On-orbit geometric calibration procedures were developed that allowed the generation of ALI geometrically corrected products that compare favorably with their Landsat 7 counterparts with respect to absolute geodetic accuracy, internal image geometry, and band registration.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2022

Assessing the accuracy and potential for improvement of the national land cover database’s tree canopy cover dataset in urban areas of the conterminous United States

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Remote Sensing↗

USGS2022

Post-fire temporal trends in soil-physical and -hydraulic properties and simulated runoff generation: Insights from different burn severities in the 2013 Black Forest Fire, CO, USA

Burn severity influences on post-fire recovery of soil-hydraulic properties controlling runoff generation are poorly understood despite the importance for parameterizing infiltration models. We measured soil-hydraulic properties of field-saturated hydraulic conductivity (Kfs), sorptivity (S), and wetting front potential (ψf) for four years after the 2013 Black Forest Fire, Colorado, USA at six sites across a gradient of initial remotely sensed burn severity using the change in the normalized burn ratio (dNBR). These measurements were correlated with soil-physical property measurements of bulk density (ρb), loss on ignition (LOI, a measure of soil organic matter), and ground cover composition to provide insight into causal factors for temporal changes in Kfs, S, and ψf . Modeled infiltration using the Smith-Parlange approach parameterized with measured Kfs, S, and ψf further discerned the role of precipitation intensity on runoff generation. Temporal trends of soil-physical properties and ground cover showed influences from initial burn severity. Trends in soil-hydraulic properties , surprisingly, were not strongly influenced by initial burn severity despite inferred effects of ρb, LOI, and ground cover on trends in Kfs and S. Calculations of dNBR at the time of sampling showed strong correlations with Kfs and S, demonstrating a new approach for estimating long-unburned Kfs and S values, infiltration model parameters after fire, and assessing the time of return to pre-fire values. Simulated infiltration-excess runoff, in contrast, did depend on initial burn severity. Time series of the ratio S2/Kfs ≈ ψf tended to converge between 1 to 10 mm four years after wildfire, potentially (i) defining a long-unburned forest domain of S2/Kfs and ψf from 1 to 10 mm with relatively high Kfs values, and (ii) providing a new post-fire soil-hydraulic property recovery metric (i.e. S2/Kfs ≈ ψf in the range of 1 to 10 mm) for sites in the Rocky Mountains of the USA.

Colorado↗

USGS2008

Radiometric calibration stability and inter-calibration of solar-band instruments in orbit using the moon

With the increased emphasis on monitoring the Earth's climate from space, more stringent calibration requirements are being placed on the data products from remote sensing satellite instruments. Among these are stability over decade-length time scales and consistency across sensors and platforms. For radiometer instruments in the solar reflectance wavelength range (visible to shortwave infrared), maintaining calibration on orbit is difficult due to the lack of absolute radiometric standards suitable for flight use. The Moon presents a luminous source that can be viewed by all instruments in Earth orbit. Considered as a solar diffuser, the lunar surface is exceedingly stable. The chief difficulty with using the Moon is the strong variations in the Moon's brightness with illumination and viewing geometry. This mandates the use of a photometric model to compare lunar observations, either over time by the same instrument or between instruments. The U.S. Geological Survey in Flagstaff, Arizona, under NASA sponsorship, has developed a model for the lunar spectral irradiance that explicitly accounts for the effects of phase, the lunar librations, and the lunar surface reflectance properties. The model predicts variations in the Moon's brightness with precision ???1% over a continuous phase range from eclipse to the quarter lunar phases. Given a time series of Moon observations taken by an instrument, the geometric prediction capability of the lunar irradiance model enables sensor calibration stability with sub-percent per year precision. Cross-calibration of instruments with similar passbands can be achieved with precision comparable to the model precision. Although the Moon observations used for intercomparison can be widely separated in phase angle and/or time, SeaWiFS and MODIS have acquired lunar views closely spaced in time. These data provide an example to assess inter-calibration biases between these two instruments.

Conference Paper↗

USGS2011

Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data

LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applications. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three different approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2) incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spectroradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS2026

Landsat 8–9 geometric and radiometric calibration and characterization

The U.S. Geological Survey Earth Resources Observation and Science Cal/Val (Calibration and Validation) Center of Excellence is a global leader in improving the accuracy, precision, and quality of remote-sensing data. Calibration is the process of quantitatively defining a system’s response to known and controlled signal inputs. Validation is the process of assessing, by independent means, the quality of the calibrated data products derived from system outputs. The Landsat Cal/Val team, comanaged by the Earth Resources Observation and Science Cal/Val Center of Excellence and the National Aeronautics and Space Administration Landsat Science Project, continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level, ensuring its reliability for scientific research. Landsat data quality is often referred to as the “gold standard” and gives other civil and commercial satellite programs a trusted reference point for measuring their own data quality. The Landsat program started more than 50 years ago. Since then, Landsat missions have gone through multiple technological advances, which, together with improved calibration and validation techniques, have led to higher data quality over time. The Cal/Val team also maintains consistency in data calibration across the multiple generations of sensors, which is vital to many scientists for time-series analysis.

Fact Sheet↗

USGS2005

Topography and Landforms of Ecuador

EXPLANATION The digital elevation model of Ecuador represented in this data set was produced from over 40 individual tiles of elevation data from the Shuttle Radar Topography Mission (SRTM). Each tile was downloaded, converted from its native Height file format (.hgt), and imported into a geographic information system (GIS) for additional processing. Processing of the data included data gap filling, mosaicking, and re-projection of the tiles to form one single seamless digital elevation model. For 11 days in February of 2000, NASA, the National Geospatial-Intelligence Agency (NGA), the German Aerospace Center (DLR), and the Italian Space Agency (ASI) flew X-band and C-band radar interferometry onboard the Space Shuttle Endeavor. The mission covered the Earth between 60?N and 57?S and will provide interferometric digital elevation models (DEMs) of approximately 80% of the Earth's land mass when processing is complete. The radar-pointing angle was approximately 55? at scene center. Ascending and descending orbital passes generated multiple interferometric data scenes for nearly all areas. Up to eight passes of data were merged to form the final processed SRTM DEMs. The effect of merging scenes averages elevation values recorded in coincident scenes and reduces, but does not completely eliminate, the amount of area with layover and terrain shadow effects. The most significant form of data processing for the Ecuador DEM was gap-filling areas where the SRTM data contained a data void. These void areas are a result of radar shadow, layover, standing water, and other effects of terrain, as well as technical radar interferometry phase unwrapping issues. To fill these gaps, topographic contours were digitized from 1:50,000 - scale topographic maps which date from the mid-late 1980's (Souris, 2001). Digital contours were gridded to form elevation models for void areas and subsequently were merged with the SRTM data through GIS and remote sensing image-processing techniques. The data contained in this publication includes a gap filled, countrywide SRTM DEM of Ecuador projected in Universal Transverse Mercator (UTM) Zone 17 North projection, Provisional South American, 1956, Ecuador datum and a non gap filled SRTM DEM of the Galapagos Islands projected in UTM Zone 15 North projection. Both the Ecuador and Galapagos Islands DEMs are available as an ESRI Grid, stored as ArcInfo Export files (.e00), and in Erdas Imagine (IMG) file formats with a 90 meter pixel resolution. Also included in this publication are high and low resolution Adobe Acrobat (PDF) files of topography and landforms maps in Ecuador. The high resolution map should be used for printing and display, while the lower resolution map can be used for quick viewing and reference purposes.

Data Series↗

USGS2025

Spatial scale dependence of error in fractional component cover maps

Geospatial products such as fractional vegetation cover maps often report overall, pixel-wise accuracy, but decision-making with these products often occurs at coarser scales. As such, data users often desire guidance on the appropriate spatial scale to apply these data. We worked toward establishing this guidance by assessing RCMAP (Rangeland Condition Monitoring Assessment and Projection) accuracy relative to a series of high-resolution predictions of component cover. We scale the 2-m and RCMAP predictions to various focal window sizes scales ranging from 30 to 1 500 m using focal averaging. We also evaluated variation in scaling effects on error at ecoregion and pasture (mean area of 1 050 ha) scales. Our results demonstrate increased accuracy at broader windows, across all components, and most increases in accuracy level off at ∼200–600 m scales. At the scale with highest accuracy, cross-component average correlation ( r ) increased by 6.5%, and root mean square error (RMSE) was reduced 46.4% relative to 30-m scale data. Scaling-related improvements to accuracy were greatest in components such as shrub and tree with more spatially heterogeneous cover and in ecoregions with more spatially heterogenous cover. When components were aggregated at the pasture scale, r increased 10% and RMSE decreased 34.3% on average relative to the 30-m scale. Our results provide empirical data on the scale dependence of error, which fractional cover data users may consider alongside their needs when using these data. Although the general principle remains that remotely sensed products are intended to address landscape-scale questions, our analysis indicates that applying data at finer than landscape spatial scales and grouping even a handful of pixels resulted in lowered error compared to pixel-level comparisons. Our results quantify the trade-offs between data granularity and error related to scale for fractional vegetation cover.

western United States↗

USGS2023

Historical Structure from Motion (HSfM): Automated processing of historical aerial photographs for long-term topographic change analysis

Precisely measuring the Earth’s changing surface on decadal to centennial time scales is critical for many science and engineering applications, yet long-term records of quantitative landscape change are often temporally and geographically sparse. Archives of scanned historical aerial photographs provide an opportunity to augment these records with accurate elevation measurements that capture the historical state of the Earth surface. Structure from Motion (SfM) photogrammetry workflows produce high-quality digital elevation models (DEMs) and orthoimage mosaics from these historical images, but time-intensive tasks like manual image preprocessing (e.g., fiducial marker identification) and ground control point (GCP) selection impede processing at scale. We developed an automated method to process historical images and generate self-consistent time series of high-resolution (0.5–2 m) DEMs and orthomosaics, without manual GCP selection. The method relies on SfM to correct camera interior and exterior orientation and a robust multi-stage co-registration approach using modern reference terrain datasets for geolocation refinement. We demonstrate the method using scanned images from the North American Glacier Aerial Photography (NAGAP) archive collected between 1967 and 1997. We present results for two sites with variable photo acquisition geometry and overlap — Mount Baker and South Cascade Glacier in Washington State, USA. The automated method corrects initial camera position errors of several kilometers and produces accurately georeferenced, high-resolution DEMs and orthoimages, regardless of camera configuration, acquisition geometry, terrain characteristics, and reference DEM properties. The average RMS reprojection error following bundle adjustment optimization was 0.67 px (0.15 m) for the 261 images contributing to 10 final DEM mosaics between 1970 and 1992 at Mount Baker, and 0.65 px (0.13 m) for the 243 images contributing to 18 individual DEMs between 1967 and 1997 at South Cascade Glacier. The relative accuracy of elevation values in the historical time series stacks was 0.68 m at Mount Baker and 0.37 m at South Cascade Glacier. Our products have reduced systematic error and improved accuracy compared to DEM products generated using SfM with manual GCP selection. Final elevation change measurement precision was ∼ "> ∼ 0.7–1.0 m over a 30-year period, enabling the study of processes with rates as low as ∼ "> ∼ 1-3 cm/yr. Our results demonstrate the potential of this scalable method to rapidly process archives of historical imagery and deliver new quantitative insights on long-term geodetic change and Earth surface processes.

Washington↗

USGS2010

Land cover characterization and mapping of continental southeast Asia using multi-resolution satellite sensor data

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.

International Journal of Remote Sensing↗

USGS2013

Evaluation of ISCCP multisatellite radiance calibration for geostationary imager visible channels using the moon

Since 1983, the International Satellite Cloud Climatology Project (ISCCP) has collected Earth radiance data from the succession of geostationary and polar-orbiting meteorological satellites operated by weather agencies worldwide. Meeting the ISCCP goals of global coverage and decade-length time scales requires consistent and stable calibration of the participating satellites. For the geostationary imager visible channels, ISCCP calibration provides regular periodic updates from regressions of radiances measured from coincident and collocated observations taken by Advanced Very High Resolution Radiometer instruments. As an independent check of the temporal stability and intersatellite consistency of ISCCP calibrations, we have applied lunar calibration techniques to geostationary imager visible channels using images of the Moon found in the ISCCP data archive. Lunar calibration enables using the reflected light from the Moon as a stable and consistent radiometric reference. Although the technique has general applicability, limitations of the archived image data have restricted the current study to Geostationary Operational Environmental Satellite and Geostationary Meteorological Satellite series. The results of this lunar analysis confirm that ISCCP calibration exhibits negligible temporal trends in sensor response but have revealed apparent relative biases between the satellites at various levels. However, these biases amount to differences of only a few percent in measured absolute reflectances. Since the lunar analysis examines only the lower end of the radiance range, the results suggest that the ISCCP calibration regression approach does not precisely determine the intercept or the zero-radiance response level. We discuss the impact of these findings on the development of consistent calibration for multisatellite global data sets.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2019

Return flows from beaver ponds enhance floodplain-to-river metals exchange in alluvial mountain catchments

River to floodplain hydrologic connectivity is strongly enhanced by beaver- ( Castor canadensis ) engineered channel water diversions. The hydroecological impacts are wide ranging and generally positive, however, the hydrogeochemical characteristics of beaver-induced flowpaths have not been thoroughly examined. Using a suite of complementary ground- and drone-based heat tracing and remote sensing methodology we characterized the physical template of beaver-induced floodplain exchange for two alluvial mountain streams near Crested Butte, Colorado, USA. A flowpath-oriented perspective to water quality sampling allowed characterization of the chemical evolution of channel water diverted through floodplain beaver ponds and ultimately back to the channel in ‘beaver pond return flows’. Subsurface return flow seepages were universally suboxic, while ponds and surface return flows showed a range of oxygen concentration due to in-situ photosynthesis and atmospheric mixing. Median concentrations of reduced metals: manganese (Mn), iron (Fe), aluminum (Al), and arsenic (As) were substantially higher along beaver-induced flowpaths than in geologically controlled seepages and upstream main channel locations. The areal footprint of reduced return seepage flowpaths were imaged with surface electromagnetic methods, indicating extensive zones of high-conductivity shallow groundwater flowing back toward the main channels and emerging at relatively warm bank seepage zones observed with infrared. Multiple-depth redox dynamics within one focused seepage zone showed coupled variation over time, likely driven by observed changes in seepage rate that may be controlled by pond stage. High-resolution times series of dissolved Mn and Fe collected downstream of the beaver-impacted reaches demonstrated seasonal dynamics in mixed river metal concentrations. Al time series concentrations showed proportional change to Fe at the smaller stream location, indicating chemically reduced flowpaths were sourcing Al to the channel. Overall our results indicated beaver-induced floodplain exchanges create important, and perhaps dominant, transport pathways for floodplain metals by expanding chemically-reduced zones paired with strong advective exchange.

Colorado↗